Few-shot Transfer Learning for Holographic Image Reconstruction using a Recurrent Neural Network

TL;DR

This paper introduces a few-shot transfer learning method for holographic image reconstruction that enables rapid generalization to new sample types with minimal data, reducing training time and computational costs.

Contribution

The authors develop a novel transfer learning approach that fixes recurrent blocks and transfers convolutional blocks, significantly reducing parameters while maintaining performance.

Findings

Achieved ~2.5-fold faster convergence.

Reduced computation time per epoch by ~20%.

Improved reconstruction performance over models trained from scratch.

Abstract

Deep learning-based methods in computational microscopy have been shown to be powerful but in general face some challenges due to limited generalization to new types of samples and requirements for large and diverse training data. Here, we demonstrate a few-shot transfer learning method that helps a holographic image reconstruction deep neural network rapidly generalize to new types of samples using small datasets. We pre-trained a convolutional recurrent neural network on a large dataset with diverse types of samples, which serves as the backbone model. By fixing the recurrent blocks and transferring the rest of the convolutional blocks of the pre-trained model, we reduced the number of trainable parameters by ~90% compared with standard transfer learning, while achieving equivalent generalization. We validated the effectiveness of this approach by successfully generalizing to new types of samples using small holographic datasets for training, and achieved (i) ~2.5-fold convergence speed acceleration, (ii) ~20% computation time reduction per epoch, and (iii) improved reconstruction performance over baseline network models trained from scratch. This few-shot transfer learning approach can potentially be applied in other microscopic imaging methods, helping to generalize to new types of samples without the need for extensive training time and data.